Online shopping platforms create an ideal environment for information cascades to occur. Utilizing real-world consumer online shopping data from Tmall, we employ panel regression analysis, instrumental variable estimation, and panel quantile regression to empirically examine information cascades in consumer decision-making and the moderating effects of product type and gender differences. Our results indicate that consumers' purchasing choices are significantly influenced by product sales rankings after controlling for network and word-of-mouth effects, as predicted by information cascade theory. Information cascades are more pronounced for experience goods than search goods, and they are more significant for female than male consumers in online product purchases. Our findings further suggest that gender differences exert a stronger moderating effect than product type on the impact of information cascades in online shopping. Specifically, information cascades are most pronounced for women purchasing experience goods online, followed by women purchasing search goods, men purchasing experience goods, and are least pronounced for men purchasing search goods.
The authors explore the influence of instructional technologies on teachers' subject-matter knowledge as well as how this knowledge is mediated by context, experience, and beliefs about teaching, mathematics, and technology. This study examined how four pre-service middle grades mathematics teachers' (PSTs) beliefs about teaching, mathematics, and technology influenced their subject matter knowledge specific to the teaching profession (i.e, specialized content knowledge [SCK]). Embedded-unit single case study analysis revealed that PSTs' different beliefs about teaching, mathematics, and technology indicated various impacts of the use of dynamic geometry software on their SCK. PSTs who viewed teaching mathematics with technology as an effective pedagogical decision to support students' understanding and self-discovery of their subject matter knowledge demonstrated more dynamic SCK with dynamic geometry software. The authors discuss interconnections between PSTs' beliefs and knowledge and share implications for technology-enhanced teacher education of middle grades mathematics teachers.
Artificial intelligence-generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolution through four developmental milestones, ranging from early rule-based systems to modern transfer learning (TL) models, within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, this article employs a common example across all milestones to illustrate the capabilities and limitations of methods within each phase, providing a consistent evaluation of AIGC methodologies and their development. Furthermore, this article addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. This study aims to guide researchers and practitioners in selecting and optimizing AIGC models to enhance the quality and efficiency of content creation across diverse domains.
Long-Term Athletic Development (LTAD) models have provided a youth sport framework for more than 2 decades, aimed at improving skill progression, training outcomes, and athlete preparation. Although influential, traditional LTAD approaches are limited by their sport-centric orientation, linear design, and focus on elite performance, leaving them less relevant for most youth and adults who do not pursue competitive sport. In response, we propose reframing LTAD as Long-Term Activity Development (LTActD), an accessible and flexible model designed to promote lifelong engagement in physical activity, including recreation, exercise, and sport. LTActD emphasizes 5 dynamic phases-Explore, Develop, Apply, Sustain, and Thrive-that accommodate diverse pathways, periods of inactivity, and opportunities to re-enter active living at any age. Represented as a curvy road, the new model underscores that participation in physical activity is rarely linear, more like a winding journey shaped by health, motivation, environment, and social context. LTActD bridges sport science and public health, positioning physical activity as a lifelong resource for health, independence, and fulfillment. LTActD offers a practical framework to empower individuals of all ages and abilities to discover meaningful ways to move, re-engage, and embrace active living throughout the life course.
Driven by accelerated product obsolescence and frequent consumer replacements, electronic waste is growing rapidly. Waste recycling, as a core component of resource reuse, has become an important means of alleviating resource scarcity and reducing environmental pollution. In the process of recycling discarded products, the efficiency of disassembly operations is crucial. To improve disassembly efficiency and maximize resource utilization, this work proposes a hybrid disassembly line structure that incorporates both linear and U-shaped workstations. Shared labor is introduced between adjacent disassembly lines, allowing workers to flexibly execute tasks across lines. This resource-sharing mechanism enhances task coordination and reduces idle time, contributing to improved system efficiency. Using a precedence relationship graph to model dependencies among tasks, we develop a mathematical model aimed at maximizing profit. We use an exact solver to verify the model and adopt a variant of dueling deep Q-network, called PER-Dueling DQN (PDDQN), which incorporates prioritized experience replay to enhance sampling efficiency and solve the model optimally. A simulation environment aligned with this problem is constructed for the reinforcement learning agent. We compare the proposed method with other reinforcement learning approaches, including advantage actor-critic, proximal policy optimization, and trust region policy optimization. Through experiments on disassembling products of different sizes, the feasibility and effectiveness of PDDQN are demonstrated, exhibiting significant advantages over other methods. Note to Practitioners-This study addresses efficiency challenges in electronic waste disassembly, where growing product turnover demands smarter resource management. A hybrid disassembly line structure combining linear and U-shaped layouts is proposed, with shared labor introduced between adjacent lines. Unlike conventional setups, this design allows workers to undertake tasks across multiple lines, improving coordination and reducing idle time. A reinforcement learning approach based on PER-Dueling DQN is used to optimize task allocation under these complex shared-resource conditions. Practitioners in remanufacturing and recycling industries can apply this method to enhance disassembly throughput, particularly in constrained environments where space and labor must be used efficiently.
